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davidanthoff/JuliaIntro.ipynb

Created May 29, 2019 15:48
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JuliaIntro.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Some random information about Julia"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you managed to get all setup problems out of the way already, you can work through this notebook to learn a bit more about Julia and some of the packages we are using in the hands on sessions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can assign values to variables with the `=` sign, and then later reference them again:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = 5"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2 * x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You call functions with the normal `f(x)` syntax, for example to print the content of variable `x`:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"println(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can create vectors with the `[]` syntax:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3-element Array{Int64,1}:\n",
" 4\n",
" 5\n",
" 6"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = [4,5,6]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use indexing notation to extract individual elements out of a vector:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that Julia uses 1-based indexing, so the first element has index 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can load a package into the currently running Julia session with the `using` command:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"using Statistics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then you can call functions from that package:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean(y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Julia supports normal strings that are in quotation marks:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"This is a string\""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = \"This is a string\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is a string\n"
]
}
],
"source": [
"println(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Julia also supports something called a symbol, that is a little like a string, but different in some not important ways. Symbols are often used to pass parameters to functions. A symbol is created with a colon:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
":foo"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = :foo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataFrames"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) is a package the provides a data type for tabular data. You can create such a new table by passing the values for each column as a vector to the `DataFrame` function (really, it is a constructor, for the computer geeks in the audience):"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"using DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>children</th><th>name</th><th>age</th></tr><tr><th></th><th>Int64</th><th>String</th><th>Float64</th></tr></thead><tbody><p>3 rows × 3 columns</p><tr><th>1</th><td>0</td><td>John</td><td>23.0</td></tr><tr><th>2</th><td>2</td><td>Mary</td><td>45.0</td></tr><tr><th>3</th><td>3</td><td>Sally</td><td>57.0</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|ccc}\n",
"\t& children & name & age\\\\\n",
"\t\\hline\n",
"\t& Int64 & String & Float64\\\\\n",
"\t\\hline\n",
"\t1 & 0 & John & 23.0 \\\\\n",
"\t2 & 2 & Mary & 45.0 \\\\\n",
"\t3 & 3 & Sally & 57.0 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"3×3 DataFrame\n",
"│ Row │ children │ name │ age │\n",
"│ │ \u001b[90mInt64\u001b[39m │ \u001b[90mString\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n",
"├─────┼──────────┼────────┼─────────┤\n",
"│ 1 │ 0 │ John │ 23.0 │\n",
"│ 2 │ 2 │ Mary │ 45.0 │\n",
"│ 3 │ 3 │ Sally │ 57.0 │"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = DataFrame(children = [0,2,3], name = [\"John\", \"Mary\", \"Sally\"], age=[23., 45., 57.])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Query.jl](https://github.com/queryverse/Query.jl) is a package that allows you to manipulate tabular data (like for example dplry in R). We can use it to for example filter a `DataFrame`:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"using Query"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.dataresource+json": {
"data": [
{
"age": 45,
"children": 2,
"name": "Mary"
},
{
"age": 57,
"children": 3,
"name": "Sally"
}
],
"schema": {
"fields": [
{
"name": "children",
"type": "integer"
},
{
"name": "name",
"type": "string"
},
{
"name": "age",
"type": "number"
}
]
}
},
"text/html": [
"<table><thead><tr><th>children</th><th>name</th><th>age</th></tr></thead><tbody><tr><td>2</td><td>&quot;Mary&quot;</td><td>45.0</td></tr><tr><td>3</td><td>&quot;Sally&quot;</td><td>57.0</td></tr></tbody></table>"
],
"text/plain": [
"2x3 query result\n",
"children │ name │ age \n",
"─────────┼───────┼─────\n",
"2 │ Mary │ 45.0\n",
"3 │ Sally │ 57.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df |> @filter(_.age>30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note how we pipe the data here from the original dataframe through a filter object. We can construct quite elaborate pipelines this way, for example we might want to save the result of the filter into a file. We can do that with the [CSVFiles.jl](https://github.com/queryverse/CSVFiles.jl) package:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"using CSVFiles"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"df |> @filter(_.age>30) |> save(\"foo.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can take a look at the documentation for these packages to learn more about their capabilities: [DataFrames docs](http://juliadata.github.io/DataFrames.jl/stable/), [Query docs](https://www.queryverse.org/Query.jl/stable/))."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are using the [VegaLite.jl](https://github.com/fredo-dedup/VegaLite.jl) package for our plotting needs in the workshop. VegaLite requires all data that we want to plot to come as a table, so we'll first create a `DataFrame` with some random numbers:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"using DataFrames, VegaLite"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>col_a</th><th>col_b</th></tr><tr><th></th><th>Float64</th><th>Float64</th></tr></thead><tbody><p>10 rows × 2 columns</p><tr><th>1</th><td>0.0113869</td><td>0.663426</td></tr><tr><th>2</th><td>0.84066</td><td>0.161438</td></tr><tr><th>3</th><td>0.86769</td><td>0.301254</td></tr><tr><th>4</th><td>0.18668</td><td>0.931829</td></tr><tr><th>5</th><td>0.879364</td><td>0.885399</td></tr><tr><th>6</th><td>0.618373</td><td>0.359592</td></tr><tr><th>7</th><td>0.637617</td><td>0.919954</td></tr><tr><th>8</th><td>0.363998</td><td>0.0611925</td></tr><tr><th>9</th><td>0.329344</td><td>0.831638</td></tr><tr><th>10</th><td>0.058039</td><td>0.77092</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cc}\n",
"\t& col\\_a & col\\_b\\\\\n",
"\t\\hline\n",
"\t& Float64 & Float64\\\\\n",
"\t\\hline\n",
"\t1 & 0.0113869 & 0.663426 \\\\\n",
"\t2 & 0.84066 & 0.161438 \\\\\n",
"\t3 & 0.86769 & 0.301254 \\\\\n",
"\t4 & 0.18668 & 0.931829 \\\\\n",
"\t5 & 0.879364 & 0.885399 \\\\\n",
"\t6 & 0.618373 & 0.359592 \\\\\n",
"\t7 & 0.637617 & 0.919954 \\\\\n",
"\t8 & 0.363998 & 0.0611925 \\\\\n",
"\t9 & 0.329344 & 0.831638 \\\\\n",
"\t10 & 0.058039 & 0.77092 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"10×2 DataFrame\n",
"│ Row │ col_a │ col_b │\n",
"│ │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │\n",
"├─────┼───────────┼───────────┤\n",
"│ 1 │ 0.0113869 │ 0.663426 │\n",
"│ 2 │ 0.84066 │ 0.161438 │\n",
"│ 3 │ 0.86769 │ 0.301254 │\n",
"│ 4 │ 0.18668 │ 0.931829 │\n",
"│ 5 │ 0.879364 │ 0.885399 │\n",
"│ 6 │ 0.618373 │ 0.359592 │\n",
"│ 7 │ 0.637617 │ 0.919954 │\n",
"│ 8 │ 0.363998 │ 0.0611925 │\n",
"│ 9 │ 0.329344 │ 0.831638 │\n",
"│ 10 │ 0.058039 │ 0.77092 │"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = DataFrame(col_a=rand(10), col_b=rand(10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then pipe this data into a definition of a plot. The definition of a plot always starts with a call to `@vlplot`. Here is a simple example:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.vegalite.v2+json": {
"data": {
"values": [
{
"col_a": 0.011386880912190422,
"col_b": 0.6634259266131171
},
{
"col_a": 0.840660109153583,
"col_b": 0.16143805536185396
},
{
"col_a": 0.8676903166589138,
"col_b": 0.30125434380042604
},
{
"col_a": 0.18668043053315175,
"col_b": 0.9318287864494113
},
{
"col_a": 0.879364127934575,
"col_b": 0.8853993027545464
},
{
"col_a": 0.6183734439241908,
"col_b": 0.35959174445828723
},
{
"col_a": 0.6376167267898476,
"col_b": 0.9199544232916976
},
{
"col_a": 0.36399755441339443,
"col_b": 0.06119252681783105
},
{
"col_a": 0.32934399814743176,
"col_b": 0.831638026009099
},
{
"col_a": 0.05803896490353666,
"col_b": 0.77092029888881
}
]
},
"encoding": {
"x": {
"field": "col_a",
"type": "quantitative"
},
"y": {
"field": "col_b",
"type": "quantitative"
}
},
"mark": "point"
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font-size: 10px; fill: #000; opacity: 1;\">0.2</text><text text-anchor=\"middle\" transform=\"translate(88.8888888888889,15)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">0.4</text><text text-anchor=\"middle\" transform=\"translate(133.33333333333331,15)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">0.6</text><text text-anchor=\"middle\" transform=\"translate(177.7777777777778,15)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">0.8</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,0)\" x2=\"200\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(100,31)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">col_a</text></g></g></g></g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,0.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; 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font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">col_b</text></g></g></g></g></g></g></g></g></svg>"
],
"text/plain": [
"VegaLite.VLSpec{:plot}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df |> @vlplot(:point, x=:col_a, y=:col_b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first argument to the `@vlplot` call here determines what kind of visual we want to plot, and then we \"bind\" (or encode) how various properties, for example the x position of each point, get their values from the data (in this case from the column called `col_a`).\n",
"\n",
"You can also store a plot in a variable. In the following two cells I'm creating two distinct plots and store them in two variables:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.vegalite.v2+json": {
"data": {
"values": [
{
"a": 0.9577808612799206,
"b": 0.22987456542154217
},
{
"a": 0.7784695657107854,
"b": 0.6063686130599766
},
{
"a": 0.7017375789518794,
"b": 0.5019958266977222
},
{
"a": 0.4028837298853778,
"b": 0.7488124547755601
},
{
"a": 0.5631713106598546,
"b": 0.5861414126133104
},
{
"a": 0.5113290973243718,
"b": 0.5782366867199948
},
{
"a": 0.9410030669644756,
"b": 0.7270261547031827
},
{
"a": 0.7075566999252778,
"b": 0.22158243385296528
},
{
"a": 0.7735675923460374,
"b": 0.29696989625846926
},
{
"a": 0.12825675771759504,
"b": 0.9766405144870403
}
]
},
"encoding": {
"x": {
"field": "a",
"type": "quantitative"
},
"y": {
"field": "b",
"type": "quantitative"
}
},
"mark": "point"
},
"image/png": 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],
"text/plain": [
"VegaLite.VLSpec{:plot}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p1 = DataFrame(a=rand(10), b=rand(10)) |> @vlplot(:point, x=:a, y=:b)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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"data": {
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"b": 1.5589085371454492
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{
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"x": {
"field": "a",
"type": "quantitative"
},
"y": {
"field": "b",
"type": "quantitative"
}
},
"mark": "point"
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],
"text/plain": [
"VegaLite.VLSpec{:plot}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p2 = DataFrame(a=10 .* rand(10), b=10 .* rand(10)) |> @vlplot(:point, x=:a, y=:b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now combine these two plots into one by using something similar to the array creation syntax:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.vegalite.v2+json": {
"hconcat": [
{
"data": {
"values": [
{
"a": 0.9577808612799206,
"b": 0.22987456542154217
},
{
"a": 0.7784695657107854,
"b": 0.6063686130599766
},
{
"a": 0.7017375789518794,
"b": 0.5019958266977222
},
{
"a": 0.4028837298853778,
"b": 0.7488124547755601
},
{
"a": 0.5631713106598546,
"b": 0.5861414126133104
},
{
"a": 0.5113290973243718,
"b": 0.5782366867199948
},
{
"a": 0.9410030669644756,
"b": 0.7270261547031827
},
{
"a": 0.7075566999252778,
"b": 0.22158243385296528
},
{
"a": 0.7735675923460374,
"b": 0.29696989625846926
},
{
"a": 0.12825675771759504,
"b": 0.9766405144870403
}
]
},
"encoding": {
"x": {
"field": "a",
"type": "quantitative"
},
"y": {
"field": "b",
"type": "quantitative"
}
},
"mark": "point"
},
{
"data": {
"values": [
{
"a": 2.9628086867698666,
"b": 1.5589085371454492
},
{
"a": 1.6993907043597956,
"b": 8.276308678049292
},
{
"a": 9.48756651482399,
"b": 7.5234328835201065
},
{
"a": 3.2102163215172586,
"b": 0.6883335184001016
},
{
"a": 5.973894419893961,
"b": 6.234258905592038
},
{
"a": 4.49085725901579,
"b": 7.828075491907365
},
{
"a": 5.085319544737628,
"b": 2.0802948080067107
},
{
"a": 5.9066895464119895,
"b": 1.6698398669303471
},
{
"a": 8.01948237118225,
"b": 4.3457258546830495
},
{
"a": 3.8530104309209046,
"b": 2.0926928586607985
}
]
},
"encoding": {
"x": {
"field": "a",
"type": "quantitative"
},
"y": {
"field": "b",
"type": "quantitative"
}
},
"mark": "point"
}
]
},
"image/png": 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"text/plain": [
"VegaLite.VLSpec{:plot}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[p1 p2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note how these two plots now have different axis. We can use VegaLite's composition features to modify this. The following example creates a plot definition that has the y axis shared between any panels in the plot, and then adds the two plots we have to this plot:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
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],
"text/plain": [
"VegaLite.VLSpec{:plot}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@vlplot(resolve={scale={y=:shared}}) + [p1 p2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can explore the documnetation for VegaLite [here](https://github.com/fredo-dedup/VegaLite.jl)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can easily define your own functions, in the following way:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"my_func (generic function with 1 method)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function my_func(x, y)\n",
" return x*y\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At that point you can call this function like any other function:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_func(3, 5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is also a shorter syntax for smaller functions:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"my_func2 (generic function with 1 method)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_func2(x,y) = x + y"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_func2(4, 6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More documentation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are done with all of this, and we are still fighting with computer problems of some of the other workshop participants, you can also start to look through the general julia [documentation](https://docs.julialang.org/en/v1.1/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.1.1",
"language": "julia",
"name": "julia-1.1"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.1.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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